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    <id>1044</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber>6 Seiten</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
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    <completedDate>--</completedDate>
    <publishedDate>2022-01-25</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Applying Learning Analytics in Online Environments: Measuring Learners’ Engagement Unobtrusively</title>
    <abstract language="eng">Prior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners’ engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students’ online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners’ engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms.</abstract>
    <parentTitle language="eng">Frontiers in Education</parentTitle>
    <identifier type="doi">10.3389/feduc.2022.840947</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-10440</identifier>
    <enrichment key="SourceTitle">Frontiers in Education, January 2022, volume 7, article 840947</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Sima Caspari-Sadeghi</author>
    <collection role="ddc" number="153">Kognitive Prozesse, Intelligenz</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Philosophische Fakultät</collection>
    <collection role="FundingAcknowledgement" number="">Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1044/Caspari_Applying_PDFA2U.pdf</file>
  </doc>
  <doc>
    <id>1223</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11 Seiten</pageNumber>
    <edition/>
    <issue>Volume 110, issue 1</issue>
    <volume>2023</volume>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-12-31</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning assessment in the age of big data: Learning analytics in higher education</title>
    <abstract language="deu">Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.</abstract>
    <parentTitle language="eng">Cogent Education</parentTitle>
    <identifier type="doi">0.1080/2331186X.2022.2162697</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-12236</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Sima Caspari-Sadeghi</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Big data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning analytics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>technology-enhanced assessment</value>
    </subject>
    <collection role="ddc" number="370">Bildung und Erziehung</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Philosophische Fakultät</collection>
    <collection role="FundingAcknowledgement" number="">Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1223/Caspari-Sadeghi_Learning_assessment.pdf</file>
  </doc>
  <doc>
    <id>1181</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>372</pageFirst>
    <pageLast>386</pageLast>
    <pageNumber>15 Seiten</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>51</volume>
    <type>article</type>
    <publisherName>SAGE Publications</publisherName>
    <publisherPlace>Sage CA: Los Angeles, CA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-24</completedDate>
    <publishedDate>2022-11-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning</title>
    <abstract language="eng">Intelligent assessment, the core of any AI-based educational technology, is defined as embedded, stealth and ubiquitous assessment which uses intelligent techniques to diagnose the current cognitive level, monitor dynamic progress, predict success and update students’ profiling continuously. It also uses various technologies, such as learning analytics, educational data mining, intelligent sensors, wearables and machine learning. This can be the key to Precision Education (PE): adaptive, tailored, individualized instruction and learning. This paper explores (a) the applications of Machine Learning (ML) in intelligent assessment, and (b) the use of deep learning models in ‘knowledge tracing and student modeling’. The paper concludes by discussing barriers involved in using state-of-the-art ML methods and some suggestions to unleash the power of data and ML to improve educational decision-making.</abstract>
    <parentTitle language="eng">Journal of Educational Technology Systems</parentTitle>
    <identifier type="issn">0047-2395</identifier>
    <identifier type="issn">1541-3810</identifier>
    <identifier type="doi">10.1177/00472395221138791</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-11818</identifier>
    <enrichment key="opus.import.date">2023-02-16T13:31:14+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
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    <enrichment key="opus.import.checksum">1ceec21ffd70e5bc31d7c56a5045b3d4</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Sima Caspari-Sadeghi</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>knowledge tracing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>technology-enhanced assessment (TEA)</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="370">Bildung und Erziehung</collection>
    <collection role="ddc" number="600">Technik, Technologie</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="institutes" number="">Sozial- und Bildungswissenschaftliche Fakultät</collection>
    <collection role="Transformationsvertrag" number="">Sage</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1181/10.1177_00472395221138791.pdf</file>
  </doc>
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